System and method for diagnosing jet engine conditions

ABSTRACT

A system and a method for diagnosis of engine conditions are proposed. In particular, the system and the method are directed to an extraction of features from different information sources and to their processing. These features, together with a series connection of two neural networks, form the crux of the system and method, so that a dependable diagnosis of engine conditions, particularly an error recognition is possible. As a result thereof, maintenance corresponding to the current engine condition is enabled.

FIELD OF THE INVENTION

The invention is directed to a system and a method for diagnosis ofengine conditions.

BACKGROUND OF THE INVENTION

Traditionally, the diagnosis of engine conditions utilizes vibrationsignals on the basis of amplitude limits. The vibration amplitude limitshave been derived from general experience and/or on the basis offeatures from vibration signatures, from the experience deriving fromevents during the development phase or, respectively, the experiencefrom the certification or testing process.

Costly and time-consuming modifications in mass production of enginesusually ensue.

The vibration diagnosis has been implemented by variously qualifiedspecialist teams without a targeted exchange of experience betweenoperators and manufacturers of engines and without systematicacquisition and interpretation of errors, side-effects or, respectively,symptoms and their causes.

In the previously standard vibration diagnosis of engine conditions,there is thus, among other things, the problem that few measuringpositions are contrasted to only a limited amount of information forinterpretation. There are in fact error catalogs from the developmentphase; these, however, are usually full of gaps. The influence of agreat number of parameters such as, for example, construction standards,tolerances, size and position of unbalanced masses, temperature effects,performance and flight parameters, etc., as well as non-linearities andmeasuring imprecisions, remain largely unconsidered.

Given this type of vibration diagnosis, dangerous vibration conditionscan continue to exist unrecognized during operation. More serioussecondary damage due to late recognition can occur and the outlay formaintenance increases since it is usually necessary to dismantle anengine.

SUMMARY OF THE INVENTION

The present invention pertains to systems and a methods for diagnosis ofengine conditions. The systems and methods are directed to extraction offeatures or parameters from different information sources and toprocessing of the features. These features, together with a seriesconnection of two neural networks, provide a dependable diagnosis ofengine conditions, particularly error recognition.

In an embodiment of the present invention, a system for diagnosis ofengine conditions has:

a means for supplying statistical/probabilistic information about theerror quota of individual engine components resulting from an evaluationof a corresponding data bank and/or

a plurality of measurement sensors for acquiring physical informationsuch as, for example, pressures and temperatures in various enginelevels and, moreover, parameters from a particle analysis in used oiland in engine exhaust gases as well as parameters from an analysis ofthe gas path;

a plurality of measurement sensors for acquiring vibration informationin the time domain from an engine;

a vibration analysis means for generating vibration information in thefrequency domain from the vibration information in the time domain;

a module for feature extraction for processing the physical informationand/or the statistical/probabilistic information and the vibrationinformation in the time and frequency domain and for the extraction of anumber of features that comprehensively describe the engine condition;

a first neural network to which the features are applied forclassification of the features, for identification of relationships anddependencies between features and for corresponding implementation of aninformation compression and for output of parameters, whereby the firstneural network comprises an input layer, one or more intermediate,layers and an output layer of neurons, whereby the input layer comprisesmore neurons than the intermediate layer(s) and this in turn comprisesmore neurons than the output layer, and the neurons of a layer areconnected via a plurality of connecting elements having variableweighting coefficients;

a first training means for supplying training input signals to the firstneural network and for comparison of the output signal output by thefirst neural network in response thereto to a training input signal andfor the modification of variable weighting coefficients of the firstneural network by means of application of a predetermined trainingalgorithm corresponding to the differences between the training inputsignal and the output signal or for realizing a non-monitored trainingof the first neural network with the assistance of the training inputsignals by themselves;

a second neural network to which the parameters output by the firstneural network are applied for classification of the parameters, forrecognition of relationships between the parameters and specific errorconstellations, for corresponding implementation of an informationlinkage and for output of a diagnosis signal, whereby the second neuralnetwork comprises an input layer, one or more intermediate layers and anoutput layer of neurons, whereby the input and the output layer comprisefewer neurons than the intermediate layer(s), and the neurons of a layerare connected to the neurons of the layer following thereupon via aplurality of connecting elements having variable weighting coefficients;and

a second training means for supplying training input signals to thesecond neural network and for comparing the output signal obtained fromthe second neural network in response thereto to a training input signaland for modifying variable weighting coefficients of the second neuralnetwork by means of applying a predetermined training algorithmcorresponding to the differences between the training input signal andthe output signal.

In an embodiment of the present invention, the module for featureextraction employs physical parameters such as oil consumption givenspecific engine runs, power reference numbers such as pressure andtemperature in specific engine levels, parameters from a particleanalysis in used oil and in engine exhaust gases as well as parametersfrom an analysis of the gas path.

In an embodiment of the present invention, the module for featureextraction employs methods that are standard for speech recognition, andextracts effective values, properties of the envelopes, modulations,absolute values, performance analyses, statistical parameters,distribution functions, wavelet analysis, etc., of the vibrationinformation in the time domain as features.

In an embodiment of the present invention, the vibration analysis meanshandles the vibration signals in the time domain and determinescorresponding vibration information in the frequency domain therefrom.

In an embodiment of the present invention, the module for featureextraction employs an information presentation in the form of what isreferred to as a waterfall diagram, handles this informationpresentation with image processing methods and determines correspondingfeatures therefrom from the vibration information in the frequencydomain.

In an embodiment of the present invention, the module for featureextraction also implements geometrical considerations of the overallimage or specific image regions; and/or the module for featureextraction also considers what are referred to as “skylines” of thewaterfall diagram from the perspective of the frequency or,respectively, of the time/speed access.

In an embodiment of the present invention, the module for featureextraction also numerically acquires the vibration information of thewaterfall diagrams; and utilizes methods from matrix and vectorcalculation or methods for system identification in the frequency domainfor acquiring features from the vibration information in the frequencydomain and/or utilizes transfer functions as well as a distributionanalysis of the numerical data.

In an embodiment of the present invention, the neural networks incombination with fuzzy logic or pure fuzzy logic circuits are providedinstead of the first and second neural networks.

In an embodiment of the present invention, a method for diagnosis ofengine conditions has the steps:

supplying statistical/probabilistic information about the error quota ofindividual engine components resulting from an evaluation of acorresponding data bank, and/or

acquiring physical information such as, for example, pressures andtemperatures in various engine levels with a plurality of measurementsensors, as well as parameters from a particle analysis in used oil andin engine exhaust gases as well as parameters from an analysis of thegas path, and/or

acquiring vibration information in the time domain from an engine with aplurality of measurement sensors;

generating vibration information in the frequency domain from thevibration information in the time domain with a vibration analysismeans;

processing the physical information and/or the statistical/probabilisticinformation and/or the vibration information in the time and frequencydomain and extracting a number of features that comprehensively describethe engine condition with a module for feature extraction;

classification of the features, identification of relationships anddependencies between features and corresponding implementation of aninformation compression and output of parameters by a first neuralnetwork to which the features are applied, whereby the first neuralnetwork comprises an input layer, one or more intermediate layers and anoutput layer, whereby the input layer comprises more neurons than theintermediate layer(s) and this in turn comprises more neurons than theoutput layer, and the neurons of a layer are connected via a pluralityof connecting elements having variable weighting coefficients;

supplying training input signals to the first neural network andcomparing the output signal output in response thereto by the firstneural network to a training input signal and modifying variableweighting coefficients of the first neural network by means ofapplication of a predetermined training algorithm corresponding to thedifferences between the training input signal and the output signal orfor realizing a non-monitored training of the first neural network withthe assistance of a training input signals by themselves with a firsttraining means;

classification of the parameters, recognition of relationships betweenthe parameters and specific error constellations, correspondingimplementation of an information linkage and output of a diagnosissignal by means of a second neural network to which the parametersoutput by the first neural network are applied, whereby the secondneural network comprises an input layer, one or more intermediate layersand an output layer of neurons, whereby the input and the output layercomprise fewer neurons than the intermediate layer(s), and the neuronsof a layer are connected to the neurons of the layer following thereuponvia a plurality of connecting elements having variable weightingcoefficients; and

supplying training input signals to the second neural network andcomparing the output signal obtained in response thereto from the secondneural network to a training input signal, and modifying; variableweighting coefficients of the second neural network by means ofapplication of a predetermined training algorithm corresponding to thedifferences between the training input signal and the output signal witha second training means.

In an embodiment of the present invention, the acquired, physicalparameters are an all consumption at specific engine runs, powerreference numbers such as pressure and temperature in specific enginelevels, parameters from a particle analysis in used oil and in engineexhaust gases as well as parameters from an analysis of the gas path.

In an embodiment of the present invention, specific engine components orparts are, for example, classified as especially susceptible in thefeature extraction on the basis of the statistical/probabilisticinformation and these information are output in the form of features.

In an embodiment of the present invention, methods as standard forspeech recognition are employed in the processing of the information andextraction of features, and effective values, properties of theenvelopes, modulations, absolute values, power analyses, statisticalparameters, distribution functions, wavelet analysis, etc., of thevibration information in the time domain are extracted as features.

In an embodiment of the present invention, the vibration information inthe time domain is processed with a vibration analysis means andcorresponding vibration information in the frequency domain aredetermined therefrom.

In an embodiment of the present invention, an information presentationin the form of what is referred to as a waterfall diagram is employedwhen processing vibration information in the frequency domain, thisinformation presentation being handled with image processing methods andcorresponding features from the vibration information in the frequencydomain being determined therefrom.

In an embodiment of the present invention, geometrical considerations ofthe overall image or of specific image regions are implemented as wellwhen processing vibration information in the frequency domain, and/orwhat are referred to as “skylines” of the waterfall diagram are alsoextracted when processing the vibration information in the frequencydomain viewed from the perspective of the frequency or, respectively, ofthe time/speed access and corresponding features are extractedtherefrom.

In an embodiment of the present invention, the information of thewaterfall diagrams are also numerically acquired when processingvibration information in the frequency domain, and methods from matrixand vector calculation or methods for system identification in thefrequency domain are utilized for acquiring vibration information in thefrequency domain and/or transfer functions as well as a distributionanalysis of the numerical data are utilized.

In an embodiment of the present invention, the classification,identification, information compression and output of parameters and theclassification, recognition of relationships, information linkage andoutput of a diagnosis signal is implemented by neural networks incombination with fuzzy logic or by pure fuzzy logic circuits instead ofby the first and second neural networks.

An object of the present invention is therefore to create a system and amethod for diagnosis of engine conditions, whereby the dependability isenhanced on the basis of a recognition of dangerous vibrationconditions, more serious secondary damage is avoided due to an earlyerror recognition, the outlay for maintenance is reduced by targetedelimination of the causes of vibration and maintenance ensues accordingto the current condition of the engine (i. e., “on-condition”).

Objects and advantages of the present invention will become apparentupon reading this disclosure including the appended claims and withreference to the accompanying drawings. The objects and advantages maybe desired, but may not necessarily be required to practice the presentinvention.

FIG. 1A is a schematic illustration of a structure of a neuralmulti-layer network for information compression;

FIG. 1B is a schematic illustration of a structure of a neuralmulti-layer network for information linkage;

FIG. 1C is a schematic illustration of a structure of a neuron unit thatis employed in the networks according to FIG. 1A and FIG. 1B; and

FIG. 2 is a block circuit diagram for illustrating a structure of theinventive system for diagnosing engine conditions.

DETAILED DESCRIPTION OF PRESENTLY PREFERRED EMBODIMENTS

Although the present invention can be made in many different forms, thepresently preferred embodiments are described in this disclosure andshown in the attached drawings. This disclosure exemplifies theprinciples of the present invention and does not limit the broad aspectsof the invention only to the illustrated embodiments.

The focus of the invention lies in a method and an apparatus for theextraction of features from different information sources and theirprocessing. These features, that characterize the engine condition incomprehensive fashion, form the crux of the system together with aseries connection of two neural networks.

Both a simulation as well as a measurement can be employed forgenerating vibration patterns (training data) for the diagnosis ofengine conditions. Both methods have their advantages and disadvantages,these being explained below.

In a simulation, it is advantageous that an analysis of variouspredefined error cases and, moreover, a combination of errors areutilized. The evaluation can thereby ensue at arbitrary positions thenumber of which is only limited by the plurality of degrees of freedomof the simulation model. Extreme, destructive cases can be analyzed.Among other things, it is advantageous to use pure signals withoutnoise. Simulations of engine runs are comparatively cost-beneficial.

What is disadvantageous in the simulation, however, is that it issubject to certain assumptions, for example in the modeling ofconnections and damping properties, etc. Included among the otherdisadvantages of simulation are the limited validity thereof, forexample only for a specific frequency band (normally the lower frequencyrange) and that some effects can only be taken into consideration withextremely great effort. Also, the simulation models only describecertain properties of the structure with a high precision. Otherproperties such as, for example, thermal influences, etc., are onlyglobally taken into consideration by contrast.

Compared to simulation, measurement has the following advantages. Theactual, current structure is employed, and no physical idealizationthereof. Particularly in the development and certification processes,certain load cases are analyzed that correspond to specific errors, forexample blade loss at various stages of the individual components of theengine. Moreover, additional operating parameters can be taken intoconsideration in measurements. A number of additional parameters can beregistered, particularly given investigations during operation.

However, problems also derive ir measurement. The imprecision or,respectively, scatter of the measurement has a disadvantageous effect,as does measuring errors or noise effects. Additionally, theindividuality of the engines and the variable reference conditions areproblematical. Observation can only be implemented at a few fixedpositions.

The following considerations thus derive for overcoming thesedisadvantages and problems.

An extensive, numerical generation of vibration identification signalsshould ensue, which should be accompanied by a generation ofexperimental signatures. A definition of parameters that are to beobserved and employed for diagnosis as well as a creation of an errorcatalog are required for this purpose. The errors to be identifiedshould be defined and an analysis of connections or, respectively, arelationship between errors should occur.

An extraction of features and an analysis of connections or,respectively, relationships between symptoms, side effects or,respectively, indications is also desired. Parameters should beidentified and engine models developed, and connections or,respectively, relationships between errors and side effects or,respectively, indications should be produced.

Further, a development of comprehensive diagnosis systems on the basisof neural networks taking various physical information (vibrations,performance features, temperature, etc.) and statistical or,respectively, probabilistic information sources into consideration isdesired.

The properties of the neural networks such as, for example, the type or,respectively, the nature, the architecture, the training method, etc.,should be defined. Over and above this, investigations about a possibleapplication of neural networks in combination with fuzzy logic shouldensue. Finally, the simulation models or, respectively, methods and themensurational techniques are optimized via sensitivity and correlationanalysis.

The main problems that occur given mensurational observations are thedata scatter, the identification of noise data, the limited data setsfor a complete analysis and the varying reference condition for eachengine. Possible solutions of the problems are a model allocation, aclassification and an identification of information with neural or,respectively, neuro-fuzzy methods.

Inventively, it is not, as traditionally, only the acquired vibrationsignals of the engine that are inventively employed for diagnosis in thediagnosis of engine conditions. Rather, other operating parameters suchas, for example, altitude, temperature, etc. that likewise co-influencethe condition of the engine are employed. Further, statistical andprobabilistic observations should be additionally taken intoconsideration.

The diagnosis of the engine condition thereby ensues upon employment ofa learning, intelligent system. This system is utilized from thedevelopment phase up to mass production. Additional physical informationsuch as operating parameters, temperatures, performance parameters,etc., are employed for the diagnosis. The current structural standardand the prior history of the engine as well as the symptoms and theirverified error causes are systematically registered and interpreted inthe inventive system and method.

In particular, the intelligent system is trained upon employment ofphysical simulation models, whereby the physical models are improvediteratively or, respectively, step-by-step upon employment of acorrelation with the measurements. In addition, the intelligent systemis trained with the assistance of actual or, respectively, real eventsand occurrences.

Moreover, the error instances of using customers or users are collectedand interpreted by the producers upon employment of a common data baseor, respectively, data bank.

Neural networks are employed in such an intelligent system. A neuralnetwork is composed of a plurality of neurons, each neuron having anon-linear input/output characteristic and being connected to oneanother by connecting elements having respectively mutually independentweighting coefficients. The weighting coefficients can be modified witha learning procedure. The output signal of the neural network iscompared to a known value (training value) on the basis of a particularcombination of input values, the known value corresponding to theseinput values. A modification of the weighting coefficients is derivedfrom this comparison such as, for example, in order to bring the outputvalue of the neural network closer to the training value. A learningalgorithm or, respectively, calculating method is employed for thispurpose. The learning process is successively repeated for a pluralityof different training values and corresponding input value combinations.This is particularly true of the neural network according to FIG. 1B inwhich training, for example, the method of monitored “back propagation”can be employed. Further, there is also the possibility of utilizingsome other method for training the neural networks. What is involved,for example, are non-monitored methods (for example, methods ofself-organizing cards of Koherson that, in particular, are employed forclassification jobs as in the neural network of FIG. 1A.

A neural multi-layer network according to FIG. 1A and FIG. 1B is formedof successive layers of neurons with intermediate connections on thebasis of connecting elements that are connected between the neurons ofone layer and neurons of preceding and following layers. The connectingelements multiply the output signals with weighting coefficientsW_(n,i,j) or, respectively, W_(n,j,k). During the training procedure ofa neural network, these weighting coefficients W_(n,i,j) or,respectively, W_(n,j,k) are capable of being modified and are determinedmutually independently. The values of the weighting coefficientsW_(n,i,j), that connect the input layer to the intermediate layer can beconsidered to be the respective coupling intensities between neurons ofthe intermediate layer U_(2,j) and the neurons of the input layerU_(1,i). When the output layer U_(3,k) of the neural network is composedof only a single neuron U_(3,l), an individual output value from thelast neuron of the neural network responding to a specific combinationof input signal values supplied to the input layer of the neural networkis generated.

FIG. 1C shows a neuron 30 that can be considered as being composed of aninput section 20 and an output section 40. The input section 20 sums upthe weighted input signal values supplied to it, whereby each of theseinput signal values is multiplied by a corresponding weightingcoefficient W_(m,i,j). The summed-up output signal that derives and thatis generated by the input section 20 of the neuron 30 is referencedX_(m,i) and supplied to the output section 40 of the neuron. The outputsection 40 carries out a processing corresponding to a non-linearfunction Y=F(x) in order to obtain the output signal Y_(n,i) that issupplied to one or more neurons of the following layer after it wasmultiplied by respective weighting coefficients.

The inventive system and method for diagnosis of engine conditions shallnow be described below with reference to FIG. 2.

Via measurement sensors 2, the inventive system and method receivesphysical information 10 such as, for example, pressure and temperaturein various levels of an engine 1 as well as parameters from the gastracking within the engine 1 and from the particle analysis in the usedoil. Further, the system receives vibration information in the timedomain 11 and, after processing thereof with a vibration analysis means3, receives information in the frequency domain 12. Additionally,information is transmitted to the system that results from statisticaland probabilistic observations 9 from data of a corresponding data bank20. Upon application of specific algorithms features 13 thatcomprehensively characterize the engine are extracted from this plethoraof information by a module for feature extraction 4.

The inventive system or, respectively, method employs a first neuralnetwork 5 that comprises more neurons in the input layer than in theoutput layer. The job of this network is to classify the suppliedfeatures 13 and, to identify relationships and dependencies between thefeatures. Groups of features are formed that are taken intoconsideration in the course of the further process on the basis ofselected “representatives”, that is the parameters 14. A datacompression is thus achieved by elimination of redundant information.

The first neural network 5 is trained by a first training means 7 uponapplication of various methods. Among others, the method of “backpropagation” with the data sets 16 and 17 is employed; however, themethod of “self-organizing cards” is also utilized.

A second neural network 6 is also provided in the inventive system andmethod. Input signals of the second, following neural network 6 are theidentifying parameters 14. The job of this second neural network is theclassification and recognition of relationships between the parameters14 and specific error constellations 15. This procedure is calleddiagnosis within the scope of the inventive system, since the errors 15are causally associated via the parameters 14 and these are in turncausally associated via the features 13 with physically interpretableproperties of the engine 1.

The various layers of the second neural network 6 are respectivelycomposed of a number of neurons. As usual in classification processes,the plurality of covered layers will be relatively low (one or twolayers). The plurality of neurons, however, will usually be greater thanthose of the outer layers. The output signal of the second neuralnetwork is a diagnosis signal which indicates a specific errorconstellation 15.

The training of the second neural network by a second training means 8is implemented with the assistance of the monitored “back propagation”method. Known errors 19 and their symptoms are thereby utilized in theform of parameters 18.

Which input signals are supplied to the module for feature extraction 4shall now be discussed in greater detail. As already mentioned above,these input data are vibration signals in the time and frequency domain11 or, respectively, 12 and, additionally, physical 10 as well asstatistical/probabilistic 9 observation parameters. These informationcomponents are separately processed in the module for featureextraction; however, common identifiers for further processing areprovided.

Regarding, the vibration signals in the time domain 11, methods andtechniques are employed which are standard for speech recognition.Moreover, the effective value “RMS=root mean square), envelopes,modulations, absolute values, performance analysis, statisticalparameters (standard deviations, etc.), distribution functions, waveletanalysis, etc. of the vibration signals in the time domain are employedas indicators.

By contrast, a presentation in the form of what is referred to as awaterfall diagram is selected for the vibration signals in the frequencydomain 12. This graphic information presentation is then handled withimage processing methods and corresponding features are sorted outtherefrom. A global observation is thus realized since the sameweighting is used for the processing of all regions of the image(waterfall diagram). Further, geometrical observations are implementedin order to generate indicators such as, for example, the center ofgravity of the overall image or center of gravities of specific imageregions that are defined according to specific physical considerations(for example, sub-harmonic or super-harmonic range). What are referredto as the “skylines” of the waterfall diagram supply additional imagefeatures viewed from the perspective of the frequency or, respectively,of the time/speed axis.

The information of the waterfall diagrams is also numerically acquired.The additional possibility thus derives of utilizing methods from matrixor vector calculation (various norms, lengths, etc.) such as, forexample, determination of maximum values, average values, aggregatenorms, Euclidian norms, correlation coefficients, regressioncoefficients, standard deviations, etc., for acquiring indicators.Further, information which allow the generation of additional featuresare extracted from the development of the amplitudes of the vibrationsallocated to the operating speed of the respective rotor, the multiplesthereof and combinations thereof. Another alternative of processing thenumerical information is the application of methods for systemidentification (direct estimation method, etc.) in the frequency domainrelated to individual spectra (i.e., quasi-constant speed) and/or to thecurves of the speed harmonics. Taking transfer functions as well as adistribution analysis of the numerical data into consideration suppliesadditional indicators from the vibration signals in the frequencydomain.

Observations of additional physical parameters 10 are a completelydifferent source of features. This group of parameters includes the oilconsumption given specific engine runs, power reference numbers such aspressure and temperature in specific engine levels, particle analysis inthe used oil and in the engine exhaust gases as well as analysis of thegas path. Another alternative derives from the statistical or,respectively, probabilistic consideration of the errors 9. Specificengine components or parts can be classified as especially susceptiblewith the assistance of this analysis. This information is employed inthe form of features.

The features 13 resulting from the corresponding module for featureextraction 4 are then the input data of the input layer of the firstneural network 5. The job of this network is the compression of thedefinitely extensive input information and the generation of largelyindependent parameters 14.

The parameters 14 output by the first neural network 5 are supplied tothe second neural network 6 and this subsequently outputs acorresponding diagnosis signal (error signal) 15.

By employing the two neural networks with the inventive system or,respectively, method, a dependable diagnosis of the engine condition canthus be achieved.

Instead of the two neural networks, neural networks can also be employedin combination with fuzzy logic or pure fuzzy logic circuits can beemployed.

While the presently preferred embodiments have been illustrated anddescribed, numerous changes, in modifications can be made withoutsignificantly departing from the spirit and scope of this invention.Therefore, the inventor intends that such changes and modifications arecovered by the appended claims.

What is claimed is:
 1. A system for diagnosis of jet engine conditions,comprising: means for supplying statistical information about an errorquota of individual jet engine components from a data bank; a pluralityof measurement sensors for acquiring physical information about the jetengine selected from pressures and temperatures in various engine levelsand parameters from a particle analysis in used oil and in engineexhaust gases as well as parameters from an analysis of the gas path; aplurality of measurement sensors for acquiring vibration information inthe time domain from the jet engine; vibration analysis means forgenerating vibration information in the frequency domain from thevibration information in the time domain; a module for featureextraction for processing the physical information and/or thestatistical information and the vibration information in the time andfrequency domain and for the extraction of a number of features thatdescribe the jet engine condition; a first neural network to which thefeatures are applied for classification of the features, foridentification of relationships and dependencies between features andfor corresponding implementation of an information compression and foroutput of parameters, whereby the first neural network comprises aninput layer, an intermediate layer and an output layer of neurons, theinput layer comprises more neurons than the intermediate layer and theintermediate layer comprises more neurons than the output layer, and theneurons of the input layer are connected to the neurons of theintermediate layer, which are connected to the neurons of the outputlayer via a plurality of connecting elements having variable weightingcoefficients; first training means for supplying training input signalsto the first neural network and for comparison of an output signal beingoutputted by the first neural network in response thereto to a traininginput signal and for the modification of variable weighting coefficientsof the first neural network; a second neural network to which theparameters output by the first neural network are applied forclassification of the parameters, for recognition of relationshipsbetween the parameters and specific error constellations, forcorresponding implementation of an information linkage and for output ofa diagnosis signal, the second neural network comprises an input layer,an intermediate layer and an output layer of neurons, the input layerand the output layer comprise fewer neurons than the intermediate layer,and the neurons of the input layer are connected to the neurons of theintermediate layer, which are connected to the neurons of the outputlayer via a plurality of connecting elements having variable weightingcoefficients; and a second training means for supplying training inputsignals to the second neural network and for comparing the output signalobtained from the second neural network in response thereto to atraining input signal and for modifying variable weighting coefficientsof the second neural network.
 2. A system according to claim 1, whereinthe module for feature extraction employs physical parameters selectedfrom a group consisting of oil consumption for specific engine runs,power reference numbers including pressure and temperature in specificengine levels, parameters from a particle analysis in used oil and inengine exhaust gases as well as parameters from an analysis of the gaspath.
 3. A system according to claim 1, wherein the module for featureextraction extracts features selected from a group consisting ofeffective values, properties of envelopes, modulations, absolute values,performance analyses, statistical parameters, distribution functions andwavelet analysis of the vibration information in the time domain.
 4. Asystem according to claim 1, wherein the vibration analysis meanshandles the vibration signals in the time domain and determinescorresponding vibration information in the frequency domain therefrom.5. A system according to claim 1, wherein the module for featureextraction employs an information presentation in the form of awaterfall diagram, handles this information presentation with imageprocessing methods and determines corresponding features therefrom fromthe vibration information in the frequency domain.
 6. A system accordingto claim 1, wherein the module for feature extraction also implementsgeometrical considerations of overall image or specific image regions.7. A system according to claim 1, wherein the module for featureextraction also numerically acquires the vibration information ofwaterfall diagrams; and utilizes methods selected from methods frommatrix and vector calculation and methods for system identification inthe frequency domain for acquiring features from the vibrationinformation in the frequency domain.
 8. A system according to claim 1,wherein neural networks in combination with fuzzy logic or pure fuzzylogic circuits are provided instead of the first and second neuralnetworks.
 9. A method for diagnosis of jet engine conditions, comprisingthe steps of: supplying statistical information about an error quota ofindividual jet engine components resulting from an evaluation of a databand; acquiring physical information about the jet engine selected frompressures and temperatures in various engine levels with a plurality ofmeasurement sensors, parameters from a particle analysis in used oil andin jet engine exhaust gases as well as parameters from an analysis ofthe gas path; acquiring vibration information in the time domain fromthe jet engine with a plurality of measurement sensors; generatingvibration information in the frequency domain from the vibrationinformation in the time domain with vibration analysis means; processingthe physical information and/or the statistical information and thevibration information in the time and frequency domain and extracting anumber of features that describe the jet engine condition with a modulefor feature extraction; classifying the features and identifyingrelationships and dependencies between features and correspondingimplementation of an information compression and output of parameters bya first neural network to which the features are applied, wherein thefirst neural network comprises an input layer, an intermediate layer andan output layer, the input layer comprises more neurons than theintermediate layer and the intermediate layer comprises more neuronsthan the output layer and the neurons of each layer are connected to theneurons of adjacent layers via a plurality of connecting elements havingvariable weighting coefficients; supplying training input signals to thefirst neural network and comparing an output signal being outputted inresponse thereto by the first neural network to a training input signaland modifying the variable weighting coefficients of the first neuralnetwork; classifying the parameters, recognition of relationshipsbetween the parameters and specific error constellations, correspondingimplementation of an information linkage and output of a diagnosissignal by means of a second neural network to which the parameters beingoutputted by the first neural network are applied, wherein the secondneural network comprises an input layer, an intermediate layer and anoutput layer of neurons, wherein the input layer and the output layerhave fewer neurons than the intermediate layer, and the neurons of eachlayer are connected to the neurons of adjacent layers via a plurality ofconnecting elements having variable weighting coefficients; supplyingtraining input signals to the second neural network and comparing anoutput signal obtained in response thereto from the second neuralnetwork to a training input signal, and modifying the variable weightingcoefficients of the second neural network.
 10. A method according toclaim 9, wherein the acquired, physical parameters are selected from allconsumption at specific engine runs, power reference numbers includingpressure and temperature in specific engine levels, parameters from aparticle analysis in used oil and in jet engine exhaust gases as well asparameters from an analysis of the gas path.
 11. A method according toclaim 9, wherein specific engine components are classified assusceptible in the feature extraction on the basis of the statisticalinformation and are output in the form of features.
 12. A methodaccording to claim 9, wherein the extracted features are selected from agroup consisting of effective values, properties of envelopes,modulations, absolute values, power analyses, statistical parameters,distribution functions and wavelet analysis of the vibration informationin the time domain.
 13. A method according to claim 9, wherein thevibration information in the time domain is processed with vibrationanalysis means and corresponding vibration information in the frequencydomain are determined therefrom.
 14. A method according to claim 9,wherein an information presentation in the form of a waterfall diagramis employed when processing vibration information in the frequencydomain, this information presentation being handled with imageprocessing methods and corresponding features from the vibrationinformation in the frequency domain being determined therefrom.
 15. Amethod according to claim 9, wherein geometrical considerations ofoverall image or of specific image regions are implemented as well whenprocessing vibration information in the frequency domain.
 16. A methodaccording to claim 9, wherein information of waterfall diagrams are alsonumerically acquired when processing vibration information in thefrequency domain, and methods selected from matrix and vectorcalculation and methods for system identification in the frequencydomain are utilized for acquiring vibration information in the frequencydomain.
 17. A method according to claim 9, wherein the classification,identification, information compression and output of parameters and theclassification, recognition of relationships, information linkage andoutput of a diagnosis signal is implemented by neural networks incombination with fuzzy logic or by pure fuzzy logic circuits instead ofby the first and second neural networks.